{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:34NESM6J2WNZQOXFCOGO2R2FYE","short_pith_number":"pith:34NESM6J","schema_version":"1.0","canonical_sha256":"df1a4933c9d59b983ae5138ced4745c128c330f2d699865be1f679a2d3a80d45","source":{"kind":"arxiv","id":"1707.09561","version":2},"attestation_state":"computed","paper":{"title":"Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.AP","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Jelena Bradic, Jue Hou, Ronghui Xu","submitted_at":"2017-07-29T21:53:35Z","abstract_excerpt":"The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size. Despite strong motivation from biomedical applications, a high-dimensional Fine-Gray model has attracted relatively little attention among the methodological or theoretical literature. We fill in this gap by developing confidence intervals based on a one-step bias-correction for a regularized estimation. We develop a theoretical framework for the partial likelihood"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1707.09561","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-07-29T21:53:35Z","cross_cats_sorted":["math.ST","stat.AP","stat.ML","stat.TH"],"title_canon_sha256":"0f8687794c3b9ca19c5f827d124a3964475a342fdeb7b08eaecd3a394a9f89e8","abstract_canon_sha256":"6708f99f48b43bc5e47d775111bd4392a03b834216ecff9089f0e5231fd87aa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:06.266495Z","signature_b64":"ri+nQ3/CrCpb3KqFjboV8WwbDg5YYBvDjP6lveOcKX1KmBzMPaG1MdTjA3zDLUMAiXbkB7dXEGDCdEOWywmXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df1a4933c9d59b983ae5138ced4745c128c330f2d699865be1f679a2d3a80d45","last_reissued_at":"2026-05-17T23:49:06.265699Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:06.265699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.AP","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Jelena Bradic, Jue Hou, Ronghui Xu","submitted_at":"2017-07-29T21:53:35Z","abstract_excerpt":"The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size. Despite strong motivation from biomedical applications, a high-dimensional Fine-Gray model has attracted relatively little attention among the methodological or theoretical literature. We fill in this gap by developing confidence intervals based on a one-step bias-correction for a regularized estimation. We develop a theoretical framework for the partial likelihood"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09561","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1707.09561","created_at":"2026-05-17T23:49:06.265845+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.09561v2","created_at":"2026-05-17T23:49:06.265845+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09561","created_at":"2026-05-17T23:49:06.265845+00:00"},{"alias_kind":"pith_short_12","alias_value":"34NESM6J2WNZ","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"34NESM6J2WNZQOXF","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"34NESM6J","created_at":"2026-05-18T12:30:58.224056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE","json":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE.json","graph_json":"https://pith.science/api/pith-number/34NESM6J2WNZQOXFCOGO2R2FYE/graph.json","events_json":"https://pith.science/api/pith-number/34NESM6J2WNZQOXFCOGO2R2FYE/events.json","paper":"https://pith.science/paper/34NESM6J"},"agent_actions":{"view_html":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE","download_json":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE.json","view_paper":"https://pith.science/paper/34NESM6J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.09561&json=true","fetch_graph":"https://pith.science/api/pith-number/34NESM6J2WNZQOXFCOGO2R2FYE/graph.json","fetch_events":"https://pith.science/api/pith-number/34NESM6J2WNZQOXFCOGO2R2FYE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE/action/storage_attestation","attest_author":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE/action/author_attestation","sign_citation":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE/action/citation_signature","submit_replication":"https://pith.science/pith/34NESM6J2WNZQOXFCOGO2R2FYE/action/replication_record"}},"created_at":"2026-05-17T23:49:06.265845+00:00","updated_at":"2026-05-17T23:49:06.265845+00:00"}